Suffern, NY - Suffern, NY, USA
Explore the pool victory probability density for each fencer, with their actual victories highlighted in a box. Learn more.
# | Name | Number of victories | ||||||
---|---|---|---|---|---|---|---|---|
0 | 1 | 2 | 3 | 4 | 5 | 6 | ||
1 | TYLER Syd | - | - | - | 2% | 11% | 36% | 51% |
2 | ZHANG Tina | - | - | 1% | 7% | 27% | 45% | 21% |
3 | KOWALSKY Rachel A. | - | - | 1% | 6% | 23% | 41% | 28% |
3 | XU Grace (XinYi) | - | - | 1% | 6% | 23% | 42% | 29% |
5 | KIM Diane E. | - | - | 1% | 6% | 23% | 41% | 29% |
6 | KUZNETSOV Victoria | - | - | - | 4% | 21% | 44% | 30% |
7 | KULKARNI Diya | - | - | 1% | 8% | 28% | 43% | 20% |
8 | REID Anousheh | - | 1% | 5% | 19% | 35% | 31% | 10% |
9 | CHIN Isabella | - | - | - | 4% | 21% | 43% | 31% |
10 | MAO Amy | - | 1% | 7% | 23% | 36% | 26% | 6% |
11 | HODGES Grace A. | - | 2% | 12% | 32% | 36% | 15% | 2% |
12 | DOUGLAS Julia F. | - | 1% | 8% | 23% | 35% | 26% | 7% |
13 | ALVIDREZ Francesca A. | - | 2% | 12% | 29% | 34% | 18% | 4% |
14 | MING Cynthia | 1% | 8% | 23% | 33% | 25% | 9% | 1% |
15 | PARTE Isabella B. | - | 3% | 12% | 29% | 34% | 18% | 3% |
16 | LI Alisha | 1% | 15% | 34% | 32% | 15% | 3% | - |
17 | NI Emma | - | - | 3% | 13% | 32% | 36% | 16% |
18 | DROVETSKY Alexandra M. | - | - | - | 4% | 20% | 44% | 32% |
19 | BLAKE Caira | - | 3% | 18% | 38% | 30% | 9% | 1% |
20 | CORDERO Allison | 5% | 21% | 34% | 27% | 11% | 2% | - |
21 | EBRAHIM Ameera H. | - | 5% | 20% | 34% | 28% | 11% | 1% |
22 | BELSLEY Devon K. | - | 1% | 8% | 23% | 35% | 26% | 7% |
23 | BIGGS Amelia F. | - | 5% | 22% | 38% | 26% | 7% | 1% |
24 | LEE Olive | - | 1% | 5% | 20% | 37% | 30% | 8% |
25 | DE JAGER Celine | - | 2% | 14% | 36% | 33% | 13% | 2% |
26 | PAN Michelle | 1% | 8% | 23% | 33% | 25% | 9% | 1% |
27 | LIVERANT Jordan S. | - | - | 3% | 15% | 34% | 35% | 13% |
28 | ZAKHAROV Anne (Anya) E. | 1% | 9% | 27% | 35% | 22% | 6% | 1% |
29 | LONG Cindy | - | - | 4% | 19% | 38% | 31% | 8% |
30 | SMOTRITSKY Mia | 2% | 15% | 37% | 32% | 12% | 2% | - |
31 | GLASSNER Sophia Rose S. | - | 6% | 31% | 40% | 19% | 4% | - |
32 | LI AnnieSiliang | 1% | 11% | 29% | 34% | 19% | 5% | - |
33 | KIZILBASH Zara | - | 4% | 19% | 34% | 29% | 12% | 2% |
34 | SMUK Daria A. | - | 1% | 8% | 25% | 37% | 24% | 5% |
35 | FAZZINI Angelina | 1% | 7% | 23% | 35% | 25% | 8% | 1% |
36 | HILL Phoebe | 4% | 18% | 33% | 29% | 13% | 3% | - |
37 | HOSANAGAR Inchara | 18% | 41% | 31% | 9% | 1% | - | - |
38 | HUH Anna | - | 2% | 16% | 36% | 33% | 11% | 1% |
39 | TOLBA Salma | - | - | 4% | 19% | 38% | 31% | 8% |
39 | SAAL Anna | - | 2% | 12% | 30% | 34% | 18% | 3% |
41 | KOKES Ava | - | 5% | 20% | 34% | 29% | 11% | 2% |
42 | CHANG Ella | 2% | 18% | 35% | 30% | 12% | 2% | - |
43 | MOK Chloe R. | 18% | 39% | 30% | 11% | 2% | - | - |
44 | NICOU Nicole | 8% | 26% | 34% | 23% | 8% | 1% | - |
45 | GOLDBERG Sophie C. | 1% | 10% | 29% | 35% | 20% | 5% | - |
46 | KIZILBASH Alizeh H. | 6% | 23% | 34% | 26% | 10% | 2% | - |
47 | APPLEBEE Andralyn | 16% | 37% | 32% | 12% | 2% | - | - |
48 | HICKS Grace | 17% | 39% | 31% | 11% | 2% | - | - |
49 | ZENG Katrina | 3% | 16% | 34% | 31% | 13% | 2% | - |
50 | PATEL Diya | 7% | 26% | 36% | 23% | 7% | 1% | - |
51 | HAND Grace | 24% | 41% | 26% | 8% | 1% | - | - |
52 | BANKULLA Misha R. | 17% | 41% | 32% | 9% | 1% | - | - |
53 | GUZZI Jordan | 8% | 29% | 36% | 20% | 6% | 1% | - |
54 | LONGSTREET Olivia | 73% | 23% | 3% | - | - | - | - |
55 | ABRAGAN Abigail | 12% | 49% | 30% | 7% | 1% | - | - |
56 | RAI Ananya | 16% | 42% | 31% | 10% | 1% | - | - |
57 | CHERNYSHOVA Victoria | 1% | 9% | 25% | 34% | 23% | 8% | 1% |
58 | BANNING Grace | 15% | 39% | 34% | 11% | 2% | - | - |
59 | GAO Judy | 1% | 8% | 24% | 34% | 24% | 8% | 1% |
60 | LEE Anna | 40% | 43% | 15% | 2% | - | - | - |
61 | XU Amy | 3% | 18% | 35% | 29% | 12% | 2% | - |
62 | RAMANATHAN Eesha | 66% | 28% | 5% | - | - | - | - |
63 | RATTRAY Katherine | 70% | 28% | 2% | - | - | - | - |
The heatmap in this table provides a visual representation of the victory probability distribution for each fencer in their respective pools:
This heatmap visualization offers an immediate understanding of each fencer's expected performance compared to their actual results.